The National lung Screening Trial has demonstrated that a 20% reduction in lung cancer mortality is associated with routine LDCT screening of older individuals with a heavy smoking history, but of the patients that had a positive screen for lung cancer based on lung nodules detected, approximately 96% proved to be false positives. These statistics highlight two unmet medical needs required to maximize the diagnostic potential of LDCT: 1) the development of diagnostic platforms that will distinguish malignant from benign nodules identified by routine LDCT, and 2) the development of inexpensive, non-invasive methods that can identify at risk individuals who would benefit from follow up with LDCT. The proposed research in Project 1 capitalizes on technical advances for assaying gene expression and abundant prior evidence that tumors are highly interactive with the immune system. Our previous studies demonstrated that it is possible to diagnose early-stage lung cancer with 90% sensitivity and 80% specificity using gene expression signatures from PBMC. The proposed research translates the PBMC diagnostic to a more clinically viable sample collection platform with the additional goal of increasing accuracy and assessing immunological processes affected by the presence or removal of a lung tumor. We present preliminary studies that support the hypothesis that this can be done. We have enriched the signature development process by assessing both mRNA and miRNA expression profiles to assess complimentary mechanisms for regulating gene expression and will also integrate Natural Killer cell and Myeloid cell markers associated with prognosis. We also introduce in Project 2 an assay for tumor associated antigens, the cancer testis antigens (CTAs) also associated with circulating tumor cells, cancer cell derived exosomes or other potentially important cells such as cancer stem cells. We provide strong preliminary evidence that detection of the mRNA for the CTA AKAP4 in PBMC derived RNA is possible and that detection is very highly correlated with the verified presence of a lung tumor. Strong preliminary results are presented for both projects. We also propose to integrate and expand the signatures from these 2 studies and assess accuracy on a single reliable platform that can assess both mRNA and miRNA expression, and is already FDA approved for a breast cancer prognosis signature, the nCounter from Nanostring.